1. Field
Example embodiments relate to an apparatus to provide an Augmented Reality (AR) service by combining a real camera image with local information provided by a location-based service, and a computer-readable medium and method of the same.
2. Description of the Related Art
Owing to the recent development of mobile communication, Location-Based Service (LBS) is attracting more interest. LBS provides specific information to a wireless Internet user or a mobile terminal (e.g. smartphone) user according to the location of the user. By providing the user with customized local information, LBS may create a variety of information. Thus LBS affords an almost unlimited potential for applicability. Examples of LBS include searching for local information about a neighborhood or a nearby facility such as a gas station, a restaurant, etc., and locating another person. As a result, LBS offers actual useful information.
A mobile terminal equipped with a camera, captures an image by the camera and projects the captured image on a display. Hence a technique to combine the captured image with local information provided by LBS and display the combination onto the display in the terminal may be implemented. Accordingly, an Augmented Reality (AR) service may be deployed. The AR is a hybrid virtual reality that adds a virtual environment into a real environment by overlaying Three-Dimensional (3D) virtual objects onto a real camera image that a user views. With AR, the user may make the most use of the advantages of LBS which links the virtual world to the real world.
Conventionally, an AR service is deployed by tracking the current location of a camera with the aid of a Base Station (BS) of a mobile communication network and combining a camera image with a virtual image in real time, or by extracting a feature point (an image patch) to detect the current location and posture of a camera and positioning a 3D graphic or 3D information at an intended position by Simultaneous Localization And Mapping (SLAM).
However, the BS-based location tracking method has limited accuracy because a BS basically covers a large area, whereas the SLAM-based feature point extraction method requires a huge amount of information to implement AR over an entire area (e.g. nationwide or worldwide) and the huge amount of information may not be stored in a terminal, because SLAM performs localization and mapping simultaneously.